Typical scenario of transcriptome data analysis consists of identification of differentially expressed genes (DEGs; those with significant changes in the number of transcripts i.e fold-changes), following by functional enrichment analysis of DEGs lists using Gene Ontology (GO). Classical GO enrichment analysis ignores the difference in the fold-changes among DEGs that leads to loss of valuable information. FoldGO method was created to identify the GO terms significantly overrepresented for the genes responded to the factor within a narrow fold-change-interval.

FoldGO processes the DEGs list in two following steps:

At the first step, FoldGO sorts the genes by their fold-change values, subdivides them into gene sets of equal size (quantiles) and generates the gene sets for all combinations of the neighboring quantiles, including the whole lists of up-regulated or down-regulated genes.Then FoldGO generates the list of GO terms annotated to at least one DEG. Optionally, GO terms significantly enriched in the DEGs annotation can be selected at this step.

At the second step, FoldGO performs enrichment analysis for all selected GO terms and for all gene sublists. FoldGO measures the bias in the portion of the genes responded fold-change-specifically versus whole DEGs for the genes associated with a GO term or the background. If the result of the test is significant, such GO terms are considered as fold-change-specific.

See an example of the FoldGO performance in the analysis of the transcriptome data on expression changes of Arabidopsis thaliana genes in response to plant hormone auxin treatment (Omelyanchuk et al., Nat Sci Rep, 2017). FoldGO is also available as an R package via Bioconductor (Wiebe, 2018).